The normal mode observations of seven quiet regions obtained by the Hinode spacecraft are analyzed to study the physical properties of granules. An artificial intelligence technique is introduced to automatically find the spatial distribution of granules in feature spaces. In this work, we investigate the dependence of granular continuum intensity, mean Doppler velocity, and magnetic fields on granular diameter. We recognized 71,538 granules by an automatic segmentation technique and then extracted five properties: diameter, continuum intensity, Doppler velocity, and longitudinal and transverse magnetic flux density to describe the granules. To automatically explore the intrinsic structures of the granules in the five-dimensional parameter space, the X-means clustering algorithm and one-rule classifier are introduced to define the rules for classifying the granules. It is found that diameter is a dominating parameter in classifying the granules and two families of granules are derived: small granules with diameters smaller than 144, and large granules with diameters larger than 144. Based on statistical analysis of the detected granules, the following results are derived: (1) the averages of diameter, continuum intensity, and Doppler velocity in the upward direction of large granules are larger than those of small granules; (2) the averages of absolute longitudinal, transverse, and unsigned flux density of large granules are smaller than those of small granules; (3) for small granules, the average of continuum intensity increases with their diameters, while the averages of Doppler velocity, transverse, absolute longitudinal, and unsigned magnetic flux density decrease with their diameters. However, the mean properties of large granules are stable; (4) the intensity distributions of all granules and small granules do not satisfy Gaussian distribution, while that of large granules almost agrees with normal distribution with a peak at 1.04 I 0.